|Year : 2018 | Volume
| Issue : 4 | Page : 721-727
Internet addiction and its mental health correlates among undergraduate college students of a university in North India
Aman Gupta1, Amir Maroof Khan1, OP Rajoura1, Shruti Srivastava2
1 Department of Community Medicine, University College of Medical Sciences and GTB Hospital, New Delhi, India
2 Department of Psychiatry, University College of Medical Sciences and GTB Hospital, New Delhi, India
|Date of Web Publication||4-Sep-2018|
Dr. Amir Maroof Khan
Department of Community Medicine, University College of Medical Sciences and GTB Hospital, New Delhi
Source of Support: None, Conflict of Interest: None
Introduction: Internet addiction (IA) is an emerging phenomenon among the youth of India. It has been found to be associated with mental health problems. This study was therefore conducted to find out the burden of IA among college students in Delhi, its risk factors and association with depression, anxiety, and stress. Methods: A cross-sectional study was conducted, with face-to-face interviews, among the nonprofessional college students of the University of Delhi. Simple random sampling was used to select the students from the list obtained from the three colleges. Young's IA test scale and depression, anxiety, and stress short scale were used to measure IA and the mental health correlates, respectively. Chi-square tests were applied for testing the association of IA with the sociodemographic variables, the variables related to internet usage patterns, and the mental health variables. Independent predictors were determined using logistic regression modeling. Results: The prevalence of IA was 25.3%. The mean (standard deviation) age of the participants was 19.1 (1.02) years and 62.1% were males. The median family income was INR 50,000. IA was significantly associated with higher family income, greater screen time, always online status, and greater duration of internet use per week. The independent predictors of IA were greater duration of internet use per week and always online status, depression, anxiety, and stress. Conclusion: The burden of IA among the college students was high. depression, anxiety, and stress were found to be independent predictors of IA.
Keywords: Addictive behavior, anxiety, depression, internet, stress disorders, students
|How to cite this article:|
Gupta A, Khan AM, Rajoura O P, Srivastava S. Internet addiction and its mental health correlates among undergraduate college students of a university in North India. J Family Med Prim Care 2018;7:721-7
|How to cite this URL:|
Gupta A, Khan AM, Rajoura O P, Srivastava S. Internet addiction and its mental health correlates among undergraduate college students of a university in North India. J Family Med Prim Care [serial online] 2018 [cited 2019 May 19];7:721-7. Available from: http://www.jfmpc.com/text.asp?2018/7/4/721/240404
| Introduction|| |
Internet addiction (IA) has been defined as “excessive or poorly controlled preoccupations, urges or behaviors regarding computer use and internet access that lead to impairment or distress.” Validated scales have been developed to quantify IA and of late, research in this domain is becoming increasingly relevant., Its risk factors and consequences have been found to be on the lines of substance abuse, and it has been recommended to include it as a specific and distinct mental health disorder in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. It is a mental health issue which has been found to be affected by the other mental health conditions.,,
The increase in penetration of high-speed internet coupled with affordability of computer devices in India over the last few years has led to emergence of IA as reported by some recent studies., There are few studies from India on the burden of IA among school children, but it's prevalence and relationship with mental health in other vulnerable groups such as college students remain to be explored. The evidence thus built will help the primary care givers in deciding to suspect and go for early screening of IA and its associated menta health attributes.
The aim of the study was to find out the prevalence of IA, and its association with depression, anxiety, and stress among the undergraduate collegiate students of a University of North India.
| Methods|| |
A cross-sectional study was conducted from November 2015 to April 2017 among the nonprofessional college students of a University of North India. This university was purposively selected as it is the largest university in the state. For the sake of feasibility, the North campus of the university was included in the study. Out of the nine colleges approached, we obtained permission from the heads of three colleges which were finally studied. The list of students was obtained from all these colleges, which formed the sampling frame of the study.
The sample size of 377, rounded off to 380, was calculated considering an estimated prevalence of IA as 43%, a 95% confidence interval and 5% confidence limit, using the Epi Info for Windows software.
As the primary objective was to estimate the prevalence of IA among the college students, representative sample sizes for each college was decided based on population proportion to size.
Simple random sampling using computer-generated random numbers were used for selecting the participants from the college wise lists of the students. Those students under 18 years of age that is below the legal age for giving consent were excluded from the study. If a particular student was not available in the college, next student on the list was selected from the same college. The study participants were contacted through their class representatives and interviews were conducted in the college premises after lectures, during lunch hour and after college hours.
A semi-structured, pretested and validated interviewer-administered questionnaire, consisting of the following four sections was used for data collection.
Section A – Sociodemographic information of the participants. The characteristics with respect to age, sex, type of family, monthly family income were recorded, type of school attended before joining college.
Section B – Internet usage patterns. This consisted of the participants' duration of computer use per week, duration of internet use per week, years since when using the computers, and the internet, type of device used frequently for internet access, type of computing devices owned, whether they were always or intermittently logged in, and so on.
Section C – Young's IA test (IAT). It is a 20-item questionnaire to screen for the presence of various degrees of IA. Cronbach's alpha for internal consistency has been reported as 0.889. It has been validated in various countries including Asian countries and has been used in some studies in India.,, The responses are based on a 5-point Likert Scale. The severity of impairment index is evaluated in the following manner: scores of 0–30 as no, 31–49 mild, 50–79 moderate, and 80–100 severe impairment. Those having moderate or severe impairments are classified as cases of possible IA.
Section D – Depression, anxiety, and stress scale (DASS-21). It is a 21-item scale with 7 items each to screen for depression, anxiety, and stress. It has been shown to have a high-internal consistency with Cronbach's alpha of 0.94 and has been found to be suitable for screening adults as shown in several international studies. It has been extensively used in India including Delhi. The response to each question varies from “Did not apply to me at all” to “Applied to me very much, or most of the time” and the scores derived from each category are multiplied by two to arrive at the final scores. All these mental health variables, namely, depression, anxiety and stress are classified into normal, mild, moderate, severe, and very severe categories based on their score cutoffs.
The data were entered in a computer-based spreadsheet. It was checked for errors and cleaned before being analyzed. SPSS 20.0 software (Armonk, NY, IBM Corp) was used for statistical computations.
Descriptive statistics in the forms of proportions and means and standard deviations (SDs) have been presented. The continuous variables such as age, monthly family income, hours of computer use per week, hours of internet use per week, were each categorized into two categories using the median value as the cutoff. Internal consistency of the Young's IAT scale was found using Cronbach's alpha.
Chi-square tests were applied for testing the association of IA with the sociodemographic variables, the variables related to internet usage patterns, and the depression, anxiety, and stress status.
Independent predictors were determined using logistic regression modeling. The risk factors which were found to have a P < 0.25 were included in the binary logistic regression analysis. Around one-third (33.16%) of the respondents refused to disclose their monthly family income, leading to a high proportion of missing values, and therefore this variable was dropped from the multivariate regression models. The two variables, i.e., screen time and duration of internet use were correlated (correlation coefficient ≥0.64) and hence could not be put together in the model. Therefore, the duration of internet use was entered in the model as it was statistically more significant in the bivariate analysis, while screen time was dropped from the model. As depression, anxiety and stress were correlated, we built three separate models including each of them respectively as independent variables. All tests were two-tailed and a P < 0.05 was considered as statistically significant.
Approval of the Institutional Ethics Committee (Human Research) of University College of Medical Sciences was obtained before the start of the study. Permissions were obtained from the heads of the respective colleges and from the copyright holders of “Young's IAT” and of “DASS-21.” Signed informed consent for participation in the study was obtained from all the respondents.
Suspected cases of mild IA, stress, and anxiety and/or depression were counseled by a trained counselor. Suspected cases of moderate and severe IA, severe or very severe stress, anxiety and/or depression based on the screening were referred to the department of psychiatry of the associated hospital for further evaluation and management.
| Results|| |
A total 477 students were approached to complete the predetermined sample size of 380, giving a response rate of 85.0%. There were 192 (50.5%), 136 (35.8%), and 52 (13.7%) students from the three colleges, as per the population proportion to size estimate.
The mean (SD) age of the participants was 19.1 (1.02) years and 62.1% were male. Around half (46.8%) were staying away from home, i.e., in hostels or rented flats or paying guest accommodations. The median (interquartile Range) of the family income was INR 50,000 (30,000–100,000) and their mean (SD) family size was 4.9 (1.99). The proportion of students in the first, second, and 3rd year of study cohorts were 28.7%, 30.3%, and 41.1%, respectively. In terms of the broad domains of the types of courses, lesser (9.5%) number of participants was pursuing Bachelor of Commerce compared to those pursuing Bachelor of Arts (47.1%) and Bachelor of Sciences (43.4%).
The majority of the students was from private (78.7%), and English medium (87.9%) schools.
The prevalence of IA was 25.3% (96/380). The level of impairment was mild among 45.0% (171/380), and 29.7% (113/380) of the participants observed were free from IA. The internal consistency of the Young's IAT scale was high (Cronbach's alpha = 0.85) in the present study.
The association of IA with certain demographic and academic characteristics is given in [Table 1].
|Table 1: Results of bivariate analysis showing the association of internet addiction with certain demographic and academic characteristics (n=380)|
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The computing devices owned by the students were smartphone (95.8%), laptop (64.0%), desktop (26.8%), and tablets (15.53%). The mean (SD) years of computer usage and internet usage were 8.4 (3.51) and 6.2 (2.73), respectively. The mean number of hours per day of screen duration, and of internet use, was 5.4 (3.28) and 4.4 (3.34), respectively.
The association of IA with certain computing and internet usage characteristics is given in [Table 2].
|Table 2: Association of internet addiction with certain computing and internet usage characteristics (n=380)|
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Those with IA when compared to those without IA, had a significantly higher (P < 0.001) proportion of subjects with depression (68.8% vs. 45.8%, odds ratio [OR] = 2.6), with anxiety (74.0% vs. 53.3%, OR = 2.3) and with stress (54.2% vs. 26.1%, OR = 3.4).
The independent predictors as determined by the multivariate logistic regression, from the three models, each for depression, anxiety, and stress are shown in [Table 3].
|Table 3: Predictors of internet addiction from the multivariate logistic regression for depression, for anxiety, and for stress (n=380)|
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| Discussion|| |
We found that around one-fourth of the study participants were addicted to the Internet. Similar findings have been reported from Mumbai in 2009 and even from studies from other countries.,, Thus, the prevalence of IA in the existing literature varies from 6% to 42%., Certain studies have reported prevalence of IA different than our study, which can be attributed to different population groups studied,, variations in cutoffs used for categorizing IA,, differences in the scales used for measuring IA.
We found no association between age and sex groups of the study participants and IA. Similar findings with respect to age and IA have been reported by studies from certain studies in India , and even from other countries.,, Many studies have reported a higher prevalence of IA among male students, both from India and abroad., Sociocultural norms might have played a role in gender difference with respect to internet access.
There are conflicting reports about the year of study course and IA. We did not find any association between the two, however, other studies have reported that the initial years of course work were associated with higher burden of IA., Other studies have reported the opposite of this, i.e., higher grades had a higher prevalence of IA. As age increases during the college years, it leads to an increase in cognitive maturity which might explain the decline in IA, whereas it also gives more freedom to the students to decide about their behaviors including internet use, which might explain the increase in IA with increasing grades.
We found no relationship between the educational background of the study participants, type of school, whether government or private, or its medium of instruction, and IA. Seyrek et al. have reported similar finding among Turkish adolescents.
In our study, there was no difference in the prevalence of IA between participants who lived at home and those living away from home. A similar finding was observed among college students of Bengaluru, but in a study from Maharashtra, those staying in private accommodations were at a higher risk of IA as compared to home. The differences in internet access, availability, and sociocultural differences may have led to this variation.
Higher family incomes have been found to be associated with a higher prevalence of IA in many studies worldwide., This is consistent with our findings too. This may have occurred because the participants who belonged to the higher income group had more resources which gave them more freedom to access the internet in the form of higher spending per month and possessing a larger number of high-end internet-enabled devices.
We found no association between the years of computer or internet use and IA. Bhatia et al reported a similar finding from among adolescents of Gwalior. However, certain other studies have reported that more number of years of internet usage was associated with higher risk for IA.,
We found that higher number of hours of internet use, and always online status, was significantly associated with increased risk of IA. The association between duration of internet use and IA has been observed in adolescents from Vadodara  and from college students of Mumbai. Outside India, similar association has been found in studies done among such diverse populations as from South East Asia,, Europe, and Middle East.
We found no association between IA and the type of device and the type of internet connection most commonly used for accessing the internet. These studies also have not found any association between these two. Some authors have reported that owning a smartphone is a significant risk factor for IA.,
We found a strong positive association between depression and IA. IA and depression were found to be significantly associated among adolescents of Mumbai. Numerous studies conducted outside India among diverse population groups in various countries have also found depression to be associated with the risk of IA.,, Depressives might find online communication easier and less intimidating than real-world communication owing to anonymity, absence of nonverbal cues and physical presence. These factors help them overcome their interpersonal difficulties, often seen in depression. Those suffering from depression may have a tendency to use the internet excessively to relieve low mood and escape the feelings of guilt and hopelessness.
We found a strong positive association between anxiety and IA. Anxiety and IA were found to be associated among college students of Mumbai. Studies done outside India have also found anxiety to be associated with the risk of IA.,, The individuals who suffer from IA may experience increased levels of anxiety as a withdrawal symptom as well as due to often found, comorbid depression. Those suffering from anxiety may start using the internet excessively as a coping mechanism to relieve dysphoric mood.
We found a strong positive association between stress and risk of IA. Other authors have also found stress to be positively associated with risk of IA, thus confirming our study finding.,, Some individuals with avoidant style of coping may start using the internet excessively to escape the stress arising out of real-world problems, leading to addiction.
| Conclusion|| |
We conclude that the prevalence of IA among undergraduate college students of was high and it is associated with depression, anxiety, and stress. IA should be considered among college students reporting to primary care physicians for common mental health problems such as depression, anxiety, and stress.
The authors would like to thank the heads of the three colleges who gave permission to conduct the study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Shaw M, Black DW. Internet addiction: Definition, assessment, epidemiology and clinical management. CNS Drugs 2008;22:353-65.
Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7:443-50.
Mak KK, Lai CM, Ko CH, Chou C, Kim DI, Watanabe H, et al.
Psychometric properties of the revised Chen Internet Addiction Scale (CIAS-R) in Chinese adolescents. J Abnorm Child Psychol 2014;42:1237-45.
Young KS. Internet addiction : The emergence of a new clinical disorder. Publ Cyberpsychol Behav 1996;1:237-44.
Block JJ. Issues for DSM-V: Internet addiction. Am J Psychiatry 2008;165:306-7.
Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al.
Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: A Cross-sectional designed study. PLoS One 2016;11:e0161126.
Park S, Hong KE, Park EJ, Ha KS, Yoo HJ. The association between problematic internet use and depression, suicidal ideation and bipolar disorder symptoms in Korean adolescents. Aust N
Z J Psychiatry 2013;47:153-9.
Thomée S, Härenstam A, Hagberg M. Computer use and stress, sleep disturbances, and symptoms of depression among young adults – A prospective cohort study. BMC Psychiatry 2012;12:176.
Yadav P, Banwari G, Parmar C, Maniar R. Internet addiction and its correlates among high school students: A preliminary study from Ahmedabad, India. Asian J Psychiatr 2013;6:500-5.
Goel D, Subramanyam A, Kamath R. A study on the prevalence of internet addiction and its association with psychopathology in Indian adolescents. Indian J Psychiatry 2013;55:140-3.
] [Full text]
Krishnamurthy S, Chetlapalli SK. Internet addiction: Prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India. Indian J Public Health 2015;59:115-21.
] [Full text]
Dean AG, Arner TG, Sunki GG, Friedman R, Lantinga M, Sangam S, et al
. Epi Info ™
, a Database and Statistics Program for Public Health Professionals. Atlanta: CDC; 2011.
Sharma A, Sahu R, Kasar P, Sharma R. Internet addiction among professional courses students: A study from central India. Int J Med Sci Public Health 2014;3:1069-73.
Gloster AT, Rhoades HM, Novy D, Klotsche J, Senior A, Kunik M, et al.
Psychometric properties of the depression anxiety and stress scale-21 in older primary care patients. J Affect Disord 2008;110:248-59.
Bhasin SK, Sharma R, Saini NK. Depression, anxiety and stress among adolescent students belonging to affluent families: A school-based study. Indian J Pediatr 2010;77:161-5.
Islam MA, Hossin MZ. Prevalence and risk factors of problematic internet use and the associated psychological distress among graduate students of Bangladesh. Asian J Gambl Issues Public Health 2016;6:11.
Kawabe K, Horiuchi F, Ochi M, Oka Y, Ueno S. Internet addiction: Prevalence and relation with mental states in adolescents. Psychiatry Clin Neurosci 2016;70:405-12.
Waldo AD. Correlates of internet addiction among adolescents. Psychology 2014;5:1999-2008.
Cheng C, Li AY. Internet addiction prevalence and quality of (real) life: A meta-analysis of 31 nations across seven world regions. Cyberpsychol Behav Soc Netw 2014;17:755-60.
Tan Y, Chen Y, Lu Y, Li L. Exploring associations between problematic internet use, depressive symptoms and sleep disturbance among Southern Chinese adolescents. Int J Environ Res Public Health 2016;13: pii: E313.
Wu CY, Lee MB, Liao SC, Chang LR. Risk factors of internet addiction among internet users: An online questionnaire survey. PLoS One 2015;10:e0137506.
Tsitsika A, Janikian M, Schoenmakers TM, Tzavela EC, Olafsson K, Wójcik S, et al.
Internet addictive behavior in adolescence: A cross-sectional study in seven European countries. Cyberpsychol Behav Soc Netw 2014;17:528-35.
Al-Gamal E, Alzayyat A, Ahmad MM. Prevalence of internet addiction and its association with psychological distress and coping strategies among university students in Jordan. Perspect Psychiatr Care 2016;52:49-61.
Seyrek S, Cop E, Sinir H, Ugurlu M, Şenel S. Factors associated with internet addiction: Cross-sectional study of Turkish adolescents. Pediatr Int 2017;59:218-22.
Cao H, Sun Y, Wan Y, Hao J, Tao F. Problematic internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health 2011;11:802.
Ak S, Koruklu N, Yılmaz Y. A study on Turkish adolescent's internet use: Possible predictors of internet addiction. Cyberpsychol Behav Soc Netw 2013;16:205-9.
Bhatia M, Rajpoot M, Dwivedi V. Pattern of internet addiction among adolescent school students of a North Indian city. Int J Community Med Public Health 2016;3:2459-63.
Chaudhari B, Menon P, Saldanha D, Tewari A, Bhattacharya L. Internet addiction and its determinants among medical students. Ind Psychiatry J 2015;24:158-62.
] [Full text]
Prabhakaran MC, Patel VR, Ganjiwale DJ, Nimbalkar MS. Factors associated with internet addiction among school-going adolescents in Vadodara. J Family Med Prim Care 2016;5:765-9.
Ho RC, Zhang MW, Tsang TY, Toh AH, Pan F, Lu Y, et al.
The association between internet addiction and psychiatric co-morbidity: A meta-analysis. BMC Psychiatry 2014;14:183.
[Table 1], [Table 2], [Table 3]